As discussed in [1]}, anomalies in time-series can generally be classified into three main categories, namely, point, contextual, and collective anomalies. However, unlike point anomalies, the definitions of contextual and collective ones are more ambiguous in the state-of-the-art, as stated in [2]}. Indeed, they are heavily impacted by the application context. For instance, Yu et al. [3]} define contextual anomalies as small temporal segments formed by neighboring points, while Golmohammadi et at. [4]} consider them as seasonal points (occurring periodically). Lai et al. have recently refined the definition of outlier types [2]}. They distinguish between point-wise outliers and pattern-wise outliers. The former is formed by global and contextual outliers while the latter is composed of shapelet, seasonal, and trend outliers. In the following, the taxonomy proposed in [2]}, which is central to our analysis, is recalled.
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